1 Gapminder Set

Let’s play with R Markdown and apply it to the previous pratical exercise

library(hrbrthemes)
library(plotly)

#1-Correlation
library(gapminder)
library(DT)
data = gapminder
datatable(data,rownames = FALSE, filter = "top", options = list(pageLength = 5, scrollX= T))
#Visualisation of the first 6 rows
head(data) #country(fct), continent(fct), year(int), lifeExp(dbl), pop(int), gdpPerCap(dbl) 
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
nrow(data) #nb of rows
## [1] 1704

1.1 Number of different year

You can also embed plots, for example:

#number of different year?
library(dplyr)
data %>%
  select(year) %>%
  unique() %>%
  nrow()
## [1] 12
library(ggplot2)
#basic scatterplot
data %>%
  filter(year =="1952")%>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) + geom_point()

1.2 Bubble Chart

p = data %>%
  filter(gdpPercap < 90000) %>%
  filter( year == 1952) %>%
  ggplot(aes(x=gdpPercap, y = lifeExp, color = continent, size = pop)) + geom_point(alpha = 0.5)+theme_ipsum()

ggplotly(p)